Shifts in rhizosphere microbial communities in Oplopanax elatus Nakai are related to soil chemical properties under different growth conditions

Plant growth environment plays an important role in shaping soil microbial communities. To understand the response of soil rhizosphere microbial communities in Oplopanax elatus Nakai plant to a changed growth conditions from natural habitation to cultivation after transplant. Here, a comparative study of soil chemical properties and microbial community using high-throughput sequencing was conducted under cultivated conditions (CT) and natural conditions (WT), in Changbai Mountain, Northeast of China. The results showed that rhizosphere soil in CT had higher pH and lower content of soil organic matter (SOM) and available nitrogen compared to WT. These changes influenced rhizosphere soil microbial communities, resulting in higher soil bacterial and fungi richness and diversity in CT soil, and increased the relative abundance of bacterial phyla Acidobacteria, Chloroflexi, Gemmatimonadetes, Firmicutes and Patescibacteria, and the fungi phyla Mortierellomycota and Zoopagomycota, while decreased bacterial phyla Actinobacteria, WPS-2, Gemmatimonadetes, and Verrucomicrobia, and the fungi phyla Ascomycota, and Basidiomycota. Redundancy analysis analysis indicated soil pH and SOM were the primarily environmental drivers in shaping the rhizosphere soil microbial community in O. elatus under varied growth conditions. Therefore, more attention on soil nutrition management especially organic fertilizer inputs should be paid in O. elatus cultivation.

Soil is a fundamental condition for plant growth and is a key component of agricultural productivity. Soil physical and chemical properties, as well as the vast rhizosphere microbial community, play a vital role in maintaining a functional balance in agroecological systems 8,9 . In terrestrial ecosystems, plants can influence the soil organic compounds and nutrient cycling they grow in, and via these chemical changes in the soil they can significantly affect other plants that subsequently grow in this soil; a phenomenon called "plant-soil feedback" effect 10,11 . In forest or agrosystem, the rhizosphere is one of the most active areas of microbial activity for the host plant. Rhizosphere microorganisms generally play a key role in participating in this feedback regulation because they can absorb organic substances from host plants and also transport nutrients and water from the soil to the plants 12 , such as some nitrogen-fixing bacteria, cyanobacteria, actinomycetes, which consequently enhances the availability of many essential nutrients and improves the stability of terrestrial ecosystems 11 . Moreover, some rhizosphere microorganisms can provide several beneficial functions for plant growth 13 , including by improving plant stress resistance, resisting soil-borne diseases, and regulating the plant immune response to enhance plant grow 14,15 . Hence, the rhizosphere microbiome has a great contribution to plant survival and growth during the initial stage under new artificial growth condition. Therefore, a better understanding the response of the rhizosphere microbial community of O. elatus to changed environments is of great interests.
In addition, the soil environment is involved in the feedback regulation of the plant-soil interface 16,17 , particularly the soil physical and chemical properties, which play a key role in shaping soil microbial structure and diversity 18,19 . Previous studies have demonstrated the process of domestication is the result of plant-microbe coevolution, and the plant-microbial interactions have been proved to contribute to the evolution of terrestrial plants 12,20 . Among the numerous environmental factors, soil pH is regarded as the primary driving factor affecting microbe structure and function. This phenomenon has been confirmed in farmland ecosystems [21][22][23] , as well as in grassland and forest ecosystem 17 . However, the driving factors may differ among various ecosystems due to variances in landscapes, land use type, latitude and longitude, and disturbance intensity 24,25 . As known, land use and human activities under cultivated conditions are higher than the wild growing environment of O. elatus, which may have a direct effect on plant growth and subsequently on soil microbial communities 26 . However, little is known about the associated soil microbe community respond to the shifted growth environment between cultivated and natural conditions in O. elatus plant.
Thus, the objective of the present study was: (I) to examine how rhizosphere microbial community structure, diversity, and the associated microorganisms respond to varied growth environments under cultivated and natural conditions; (II) to analyze the relationship between the characteristics of the soil microbial community and soil chemical properties and identify factors driving factors driving the rhizosphere soil microbial community change in the artificial cultivation of O. elatus.

Results
Variance analysis of soil physicochemical properties. The soil chemical properties of the six samples of each treatment are shown in Table 1. The ANOVA for rhizosphere soil properties showed significant (P < 0.05) differences in pH, SOM (soil organic matter), and AN (Available nitrogen) in samples from WT and CT. According to the results, soil pH was 4.92 in CT, which was significantly higher than in WT. In addition, soil samples in CT had a significantly lower SOM and AN content, while no significant (P > 0.05) differences in AP (Available phosphate) and AK (Available potassium) content were observed between soil samples from WT and CT (Table 1).
Sequencing analysis and alpha diversity. Using an Illumina MiSeq HTS platform, a total number of 667,662 merged sequences for bacteria and 55,279 for fungi were obtained (Supplemental Table 1). The number of operational taxonomic units (OTUs) in CT and WT was 11,268 and 10,923 for bacteria rhizosphere communities, respectively, and the number of combined OTUs in all samples was 586 (Fig. 1). Correspondingly, the total number of OTUs was 1,462 and 843 in CT and WT for the fungi community, and the number of combined OTUs in all samples was 101.
To assess the diversity and evenness of microbial populations variations of soil samples from CT and WT, the Chao_1,Observed_species, Pielou_e, and Shannon index were analyzed (Table 2). For bacteria communities, significantly higher Chao_1, Observed_species, and Shannon index values were found in CT compared to WT. Correspondingly, the Chao_1, Observed _species, Pielou_e and Shannon index showed a similar trend in the fungi communities under the two treatments, which indicated that cultivated conditions had significant effect on the soil bacterial and fungi richness and diversity.
At the bactirial class level, a total of 14 classes had a relative abundance > 1% for each sample, among the 10 most abundant classes (Fig. 3a), the relative abundance of Gammaproteobacteria, Actinobacteria, AD3, Bacilli, Gemmatimonadetes, Deltaproteobacteria, and Thermoleophilia were greatly increased in CT, while that of Alphaproteobacteria, Acidobacteriia and WPS-2 was decreased and that of Bacteroidia and Subgroup_6 were was unchanged, compared to WT.
At the fungi class level, a total of 10 classes had a relative abundance > 1% for each sample, among the 10 most abundant classes, the relative of Leotiomycetes, Mortierellomycetes, Sordariomycetes, unidentified and unclassified_Fungi were greatly increased in CT, while that of Agaricomycetes and Archaeorhizomycetes was significantly decreased, compared to WT (Fig. 3b).

Relationships of the microbial communities among the different rhizosphere soil samples.
To investigate the relationships of the rhizosphere soil basic characteristics and microbial community properties, principal components analysis (PCA) was performed among samples from CT and WT at the genus level. For bacterial communities, 12 samples were divided into two groups, the PCA score plot showed that the six samples from CT grouped to the right of the graph along the first principal component (PC1), accounting for 56% of the   (Fig. 4a). For fungal communities, the axes of PC1 and PC2 explained 44% and 22.8% of the sample variance (Fig. 4b). A hierarchically clustered heatmap was further constructed to evaluate the relationships among the samples at the genus level based on the top 20 most abundant microbial communities. The result showed that the 12 soil samples could be separated into two groups (Fig. 5a). The most abundant genera were AD3 (9.28%) in CT (in red) and Subgroup_2 ( (Fig. 5b). The heatmap analyses (Fig. 4) agree with the results of the PCA (Fig. 5a), with both analyses indicating that soil samples in CT had a great effect on the rhizosphere soil characteristics and microbial communities.
Correlation analysis of soil microbial community with soil chemical properties. The RDA was conducted to analyze the correlation between soil microbial community and soil chemical properties (Fig. 6). The output of the RDA explained 53.25% of the total variation of the bacterial community (Fig. 6a) and 34.62% of fungal community variation (Fig. 6b). For the bacteria community, the RDA analysis indicated soil pH played an important role in the structure of the bacterial community, followed by the content of AN, SOM, AP and AK. Of the five parameters, pH had large positive values on RDA1, and SOM has large positive values on RDA2 (Fig. 6a). For the fungal community, the soil chemical properties of SOM and pH had a major impact on the soil structure of the fungal community, followed by the content of AN, AK and AP, while pH had large positive values on RDA1, SOM, AN and AK have large positive values on RDA2 (Fig. 6b).

Discussion
Artificial breeding is crucial to the protection and utilization of endangered wildlife resources. When plant transplanted form the natural habitats to a controlled growth environment, soil quality is one of the critical factors affecting plant growth and development. In the present study, ANOVA analysis revealed significant difference in soil pH, the content of SOM and AN between WT and CT. Compared to WT, soil samples in CT had a higher pH, but a lower content of soil SOM and AN. As known, land use patterns is one of the important factors affecting soil nutrient availability 27 . Compared to forest, the long-term low amount of carbon input and high deposition rate in cultivated conditions may be the main reason for lower SOM in CT conditions 28,29 . Moreover,    Figure S1). However, more detailed information about the relationship on soil supply and O. elatus plant nutrient requirements should be investigated in future. The environmental conditions under specific land use models have great influence on soil microbial communities and diversity 10 . It has been well documented the bacterial communities in soil with stable environment under natural habitats are more sensitive to changed soil conditions than those from more variable soil environment 31,32 . In the present study, significant differences in soil bacterial alpha diversity were observed between CT and WT, and the higher Chao_1, Observed_species, and Shannon index values were found in CT. This is in agreement with previous findings that land-use intensity increased the soil bacterial diversity 22,33 . Normally, higher land use management exerts negative effects on the inhabiting microbe species and decreases the soil fungi diversity 34 . Previous literatures have shown that soil fungi diversity in forest system is mainly regarded for stand structure of plant canopy, decomposition stage of deadwood in ground surface, as well as anthropogenic  www.nature.com/scientificreports/ disturbances 30,34 . To stimulate the natural habitat growth environment of O. elatus plant, organic fertilizer was added to the CT soil in this study. The additional organic compounds and residual fertilizer from the previous crop could result in the increase in fungal diversity under CT treatment. Similar results of agricultural management have been demonstrated in farmland ecosystems, such as short-term straw incorporation and manure application caused increase in soil microbial diversity 23,32 . Plant growth environmental factors, particularly soil chemical properties of pH 21 , organic matter content, and C:N rate are the mainly driving force of soil microbial communities responded to the changed envrioment 35,36 . The results of our study revealed that the structure of soil bacterial and fungal communities differed between the CT and WT treatments. Compared to WT, the three dominant bacterial phyla of Acidobacteria, Chloroflexi, Gemmatimonadetes, and two non-dominant (Firmicutes, Patescibacteria) bacterial phyla, were enhanced, and the three dominant bacterial phyla of WPS-2, Gemmatimonadetes, and Verrucomicrobia and one non-dominant bacterial phyla of Actinobacteria were decreased. Members of the Acidobacteria bacterial phylum are pervasive and copiously distributed across nearly all ecosystems. They act as plant growth-promoting bacteria, and a shift in these rhizosphere soil microbiota reflects the plant-soil feedback regulation under CT conditions. A similar study in agroecosystem reported that continuous monoculture cultivation resulted in compositional changes in the soil microbiota in rice plants 37 . Chloroflexi and Firmicutes bacteria usually constitute a substantial proportion of the bacterial communities in rhizosphere soils and may play an important role in utilizing geochemical inputs such as sulfide from upslope weathering 16 . Gemmatimonas is a very slow-growing bacterium which is able to utilize substrates as sole carbon sources for energy. This is beneficial for plant growth under a changed habitat (e.g., soil pH, PM content) under CT conditions. This also corroborates with the previous study that cold and water-saturated environment induced a high relative abundance of novel Chloroflexi, which can act as a stable and resistant life-strategy in response to abiotic environmental conditions in alpine tundra wet meadow soil 38 .
At the fungal phylum level, the relative abundance of Mortierellomycota and Zoopagomycota in CT was significantly enhanced. Mortierellomycotina and Zoopagomycota are a group of early divring fungi that are frequently associated with plant rhizospheres 15 . The increase in rhizosphere Mortierellomycotina and Zoopagomycota may be associated with the microbial community structure resilience under changed environmental conditions 11 . Most soil-inhabiting Basidiomycota are associated with woody plants and live in colonized natural or relatively undisturbed forests 39 . The reduced relative abundence of Mortierellomycotina indicated the high intensity land use environment may unfavorable to some types of fungal microbes.
To our knowledge, soil quality has a significant effect on the aggregation of rhizosphere bacteria and fungi communities. In turn, soil microbial community composition is closely related to the changes of soil available nutrients 23 . In northeastern China, soil pH is regarded as the primary factor driving the distribution and function of microorganisms in farmland soils 21 . Increasing evidence based on the combination of 16S rRNA sequencing data and corresponding functional profiles from 150 forest and 150 grassland soils also proved that soil pH is the best predictor for bacterial community structure, diversity, and function in temperate grasslands and forests 17 . These results are consistent with the present study, in that soil pH played an important role in shaping the structure of the bacterial and fungi community under short-term domesticated cultivation (Fig. 6, Fig.  S3). In agroecosystems, the availability of SOM is a complex bio-mediated process involving soil microbes, but it is also considered as an overarching edaphic factor dominating soil microbial diversity 40 . The RDA analysis indicated, SOM played a key role in influencing soil microbial community structure ( Fig. S3 and S4). This result corroborates a previous study that found that the mineralization of SOM is a complex bio-mediated reaction in which organic substrates are converted into living biomass and mineral residues 25 , thus providing substance and energy to the soil microbiome. Therefore, more attention to soil nutrition management especially organic fertilizer inputs should be paid in O. elatus artificial cultivation.

Conclusion
In this study, we compared the rhizosphere soil chemical properties and soil microbial communities of O. elatus plant growth in natural and cultivated conditions. We found the soil in cultivated conditions had higher pH and lower content of soil organic matter (SOM) compared to natural habitat. The changed growth environment caused by transplant significantly influenced the structure and diversity of rhizosphere soil microbial community. Compared to the natural habitat, the cultivated conditions resulted in a higher soil bacterial and fungi richness and diversity in Changbai Mountain, Northeast of China. Our study showed that 32 phyla, 90 classes, 257orders and 454 families in the rhizosphere bacterial community and, 16 phyla, 34 classes, 116 orders and 272 families in the fungal community were influenced by changed growth conditions. RDA analysis between soil microbial community and soil chemical properties revealed that soil pH and SOM may act as the main factor on shaping the rhizosphere soil microbial community of O. elatus in cultivated conditions.

Materials and methods
Site description and artificial cultivation experimental design. The wild growth site was located in the forest of Shisidaogou town (Fig. 7), Changbai Korean Autonomous County (41°28′46″N, 128°2′23″), which belongs to the Changbai Mountain Nature Reserve of China, with an altitude of 836 m and an atmospheric pressure of 91.68 kPa. This region has a huge forest coverage of 87.9%. The climate is a north monsoon climate that is greatly impacted by topography, with a long cold winter and short, warm, and humid summer. The mean annual temperature ranges from − 7 °C to 3 °C, the lowest temperature in severe winter can reach − 40 °C, the annual sunshine duration is 2300 h, and the mean annual precipitation is 700-1400 mm.
The cultivated site is not far from the wild site (Fig. 7). The previous crop at the experimental site was maize with 2 years continuous planting. Before O. elatus cultivation, all the weeds and shrubs in the planting area were cut down and removed in the fall of last year, then plenty of burrow-shaped land with area of 1 m × 1 m, and Seed germination and transplant. The seeds from wild plants soaked in warm water at 40 °C for 2 days, then transferred to 1% CuSO 4 solution for 2 to 3 h, rinsed with five times with distilled water. Then, the seeds evenly mixed with the fine sand at the ratio of 1:3 (seed/sand).After that, the treated seeds were placed in a condition controlled incubator at the temperature of 18 ± 0.5 °C, humidity 60% for 4 months, and then put into another chamber at 4 °C for 3 months. In the spring of the following year, the treated seeds were put in a disc with 4 layers of filter paper for seed germinated at temperature of 25 °C. Finally, the seeds were sown in the soil in natural habitat site. At the spring of 2020, the young seedlings of O. elatus were transplanted to the farm in cultivated conditions, then all the soil around the plants was covered with a layer of leaves, and the light transparency was controlled at 60%.
Collection and preparation of rhizosphere soil. Soil sampling was carried out in July 2021 at the Changbai Mountain in Northeast China, two years after transplantation. Rhizosphere soil is defined as the portion of soil found adjacent to the roots of living plants and influenced by root activity. At each site, six healthy plants were randomly selected, then the soil was carefully excavated from around single plants down to approximately 25 cm (root depth most no more than 15 cm), the root was carefully and completely sampled, the loose soil was gently removed by shaking the roots, and the rhizosphere soil adhering to the roots was collected about 50-80 g separately from the root surface using a brush. Each soil sample was sieved through a 2-mm sieve after removing roots and rocks and divided into two parts: one of which is about 10 g was carefully placed into sterile ziplocked bags, immediately frozen, and stored at − 80 °C to extract total soil DNA for high-throughput sequencing and soil microbial biomass measurements, and the remaining parts was air-dried for measurements of available nutrients and pH.
Soil chemical analysis. Soil organic carbon (SOC) was determined by the dichromate oxidation method and followed the instructions of a previous study 29 , and soil organic matter content (SOM) was calculated based on the content of SOC multiply a coefficient of 1.724 41,42 . Soil available nitrogen (AN) was measured by alkaline hydrolysis diffusion method and following the description of Chen et al 43 . Available phosphorus (AP) was determined by the molybdenum-antimony colorimetric method after sodium bicarbonate extraction 44 . and available potassium (AK) were extracted using 1 M NH 4 OAc, pH 7 solution and determined by a flame photometer (Sherwood Scientific, M410 C, UK) 42 . Soil pH was measured in a 1:5 soil: deionised water suspension using a pH meter (DDS-307, Wuxi Leixi Instrument Co., Ltd., China).   (5′-GGA AGT AAA AGT CGT AAC AAGG -3′) and ITS2 (5′-GCT GCG TTC TTC  ATC GAT GC-3′) 45  Statistical analyses. The sequenced data was performed using QIIME 2 2019.4 46 with slight modification.
Briefly, raw sequence data were demultiplexed using the demux plugin followed by primers cutting with cutadapt plugin. Sequences were then merged, filtered and dereplicated using functions of fastq_mergepairs, fastq_ filter, and derep_fulllength in Vsearch. All the unique sequences were then clustered at 98% (via cluster_size) followed by chimera removing. At last, the non-chimera sequences were re-clustered at 97% to generate OTU representive sequences and OTU table. Representive sequences were aligned with mafft and used to construct a phylogeny with fasttree2 47 . Alpha-diversity metrics (Chao1, Observed species, Shannon, Pielou's evenness), beta diversity metrics (Bray-Curtis dissimilarity) were estimated using the diversity plugin with samples were rarefied. Taxonomy was assigned to ASVs using the classify-sklearn naïve Bayes taxonomy classifier in featureclassifier plugin against the Silva v132 99% OTUs reference sequences 48 . Venn diagrams were generated using the VENNY online program (http:// bioin fogp. cnb. csic. es/ tools/ venny/). Hierarchical clustering heatmap of the relative abundance of the 20 most abundant bacterial and fungi genera were created using the online program (https:// www. genes cloud. cn/ chart/ HeatM ap). Redundancy analysis (RDA) between soil microbe community structure and the soil chemical properties was conducted using genescloud tools (https:// www. genes cloud. cn); a free online platform for data analysis. All the basic statistical analyses were also performed by the genescloud tools, and one-way ANOVA of soil chemical properties between two treatments was conducted using IBM SPSS 24.0 statistical software (IBM Corp., Armonk, NY, USA).